Predicting phenotypic and genotypic evolution given fluctuating selection

Introduction

Introduction

Introduction

Introduction

Allele Frequency change at seasonal SNPs

Aims

• Develop a polygenic model that accurately predicts genotypic and phenotypic evolution featuring fluctuating selection

Genome

Genome

• Phenotype is the sum of all alleles’ effect plus random noise

Parameters

WF model with 5 replicates each having the following parameters:

• Heritability: 0.1, 0.5, and 0.8

Parameters

WF model with 5 replicates each having the following parameters:

• Heritability: 0.1, 0.5, and 0.8
• Loci numbers: 1, 10, 70, 100, 300

Parameters

WF model with 5 replicates each having the following parameters:

• Heritability: 0.1, 0.5, and 0.8
• Loci numbers: 1, 10, 70, 100, 300
• Standard deviations: 1, 2, 3, 4

Parameters

WF model with 5 replicates each having the following parameters:

• Heritability: 0.1, 0.5, and 0.8
• Loci numbers: 1, 10, 70, 100, 300
• Standard deviations: 1, 2, 3, 4
• Generation interval (GI): 10, 20, 30 (Linear changes)

Parameters

WF model with 5 replicates each having the following parameters:

• Heritability: 0.1, 0.5, and 0.8
• Loci numbers: 1, 10, 70, 100, 300
• Standard deviations: 1, 2, 3, 4
• Generation interval (GI): 10, 20, 30 (Linear changes)
• GI: sinusoidal1 (20) and sinusoidal2 (5, 10, 15, 30)

Models

• Neutral selection

• Constant selection

• Fluctuating selection(Linear & Sinusoidal)

Fitness Function

\[ \text{Fitness} = \exp\left(-\frac{1}{{\text{phenotype}}} \times (\text{phenotype} - \text{optimum})^2\right) \]

• Optimum = mean(phenotype) ± nSD

Fitness Function

\[ \text{Fitness} = \exp\left(-\frac{1}{{\text{phenotype}}} \times (\text{phenotype} - \text{optimum})^2\right) \]

• Optimum = mean(phenotype) ± nSD

Genotype file

Phenotype file

Alle Frequency (single plot FS)

Allele Frequency (single model)

Mean Allele Frequency (all variables)

Phenotypes (single plot FS)

Phenotypes (single model)

Phenotypes (all variables)